RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation

Abstract

Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems—graph construction, representation learning, and real-time serving—yet existing work addresses each in isolation. We present RankGraph-2, a framework deployed at Meta that co-designs all three lifecycle stages for similarity-based retrieval (U2U2I and U2I2I), where each stage’s requirements shape the others. Serving requires a co-learned cluster index to avoid expensive online KNN—this pushes index co-training into the training objective. Training benefits from the observation that similarity-based retrieval tolerates pre-computed neighborhoods, eliminating online graph infrastructure—this requires construction to produce self-contained data. Construction must also support hour-level refresh for item coverage. Acting on these cascading requirements, RankGraph-2 reduces hundreds of trillions of edges to hundreds of billions via subsampling with popularity bias correction, pre-computes multi-hop neighborhoods via personalized PageRank, and co-learns a residual-quantization cluster index that reduces serving computational cost by 83%. This lifecycle co-design enables a simple architecture to achieve 3.8 × higher recall than a GAT + Deep Graph Infomax model on a bipartite graph and 2.1 × higher than PyTorch-BigGraph on item retrieval. RankGraph-2 delivers up to +0.96% CTR and +2.75% CVR, and has powered 20+ retrieval launches across major surfaces.